The architecture of the proposed model is depicted in Fig. REF . Multi-scale predictions are learned from different layers with different receptive field sizes (see Fig. REF (a)). For obtaining such multi-scale predictions, supervision is directly fed into corresponding layers (see Fig. REF (b)). Such deep supervision learning strategy boosts the performance via: 1) directly producing multi-scale saliency predictions; and 2) improving discriminativeness of intermediate layers, thus gaining improvement of overall performance, as demonstrated in [1]}.
